import argparse
import pathlib

import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.models.roberta.modeling_roberta import RobertaAttention
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("""1.0.0a"""):
		raise Exception("""requires fairseq >= 1.0.0a""")

logging.set_verbosity_info()
__a    :	List[Any]         =    logging.get_logger(__name__)

__a    :	List[str]         =    """Hello world! cécé herlolip"""





def 							UpperCAmelCase		(       lowercase				,     lowercase				,     lowercase				):







				"""simple docstring"""



				__lowercase					     =				FairseqRobertaModel.from_pretrained(lowercase				)
				roberta.eval()  # disable dropout
				__lowercase					     =				roberta.model.encoder.sentence_encoder
				__lowercase					     =				XLMRobertaConfig(
				    vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings				,     hidden_size=roberta.cfg.model.encoder_embed_dim				,     num_hidden_layers=roberta.cfg.model.encoder_layers				,     num_attention_heads=roberta.cfg.model.encoder_attention_heads				,     intermediate_size=roberta.cfg.model.encoder_ffn_embed_dim				,     max_position_embeddings=514				,     type_vocab_size=1				,     layer_norm_eps=1E-5				,     )
				if classification_head:
								__lowercase					     =				roberta.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

				print('''Our RoBERTa config:'''				,     lowercase				)

				__lowercase					     =				XLMRobertaXLForSequenceClassification(lowercase				) if classification_head else XLMRobertaXLForMaskedLM(lowercase				)
				model.eval()

				# Now let's copy all the weights.
				# Embeddings
				__lowercase					     =				roberta_sent_encoder.embed_tokens.weight
				__lowercase					     =				roberta_sent_encoder.embed_positions.weight
				__lowercase					     =				torch.zeros_like(
				    model.roberta.embeddings.token_type_embeddings.weight				)  # just zero them out b/c RoBERTa doesn't use them.

				__lowercase					     =				roberta_sent_encoder.layer_norm.weight
				__lowercase					     =				roberta_sent_encoder.layer_norm.bias

				for i in range(config.num_hidden_layers				):
								# Encoder: start of layer
								__lowercase					     =				model.roberta.encoder.layer[i]
								__lowercase					     =				roberta_sent_encoder.layers[i]

								__lowercase					     =				layer.attention
								__lowercase					     =				roberta_layer.self_attn_layer_norm.weight
								__lowercase					     =				roberta_layer.self_attn_layer_norm.bias

								# self attention
								__lowercase					     =				layer.attention.self
								assert (
								    roberta_layer.self_attn.k_proj.weight.data.shape
								    == roberta_layer.self_attn.q_proj.weight.data.shape
								    == roberta_layer.self_attn.v_proj.weight.data.shape
								    == torch.Size((config.hidden_size, config.hidden_size)				)
								)

								__lowercase					     =				roberta_layer.self_attn.q_proj.weight
								__lowercase					     =				roberta_layer.self_attn.q_proj.bias
								__lowercase					     =				roberta_layer.self_attn.k_proj.weight
								__lowercase					     =				roberta_layer.self_attn.k_proj.bias
								__lowercase					     =				roberta_layer.self_attn.v_proj.weight
								__lowercase					     =				roberta_layer.self_attn.v_proj.bias

								# self-attention output
								__lowercase					     =				layer.attention.output
								assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
								__lowercase					     =				roberta_layer.self_attn.out_proj.weight
								__lowercase					     =				roberta_layer.self_attn.out_proj.bias

								# this one is final layer norm
								__lowercase					     =				roberta_layer.final_layer_norm.weight
								__lowercase					     =				roberta_layer.final_layer_norm.bias

								# intermediate
								__lowercase					     =				layer.intermediate
								assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
								__lowercase					     =				roberta_layer.fca.weight
								__lowercase					     =				roberta_layer.fca.bias

								# output
								__lowercase					     =				layer.output
								assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
								__lowercase					     =				roberta_layer.fca.weight
								__lowercase					     =				roberta_layer.fca.bias
								# end of layer

				if classification_head:
								__lowercase					     =				roberta.model.classification_heads['''mnli'''].dense.weight
								__lowercase					     =				roberta.model.classification_heads['''mnli'''].dense.bias
								__lowercase					     =				roberta.model.classification_heads['''mnli'''].out_proj.weight
								__lowercase					     =				roberta.model.classification_heads['''mnli'''].out_proj.bias
				else:
								# LM Head
								__lowercase					     =				roberta.model.encoder.lm_head.dense.weight
								__lowercase					     =				roberta.model.encoder.lm_head.dense.bias
								__lowercase					     =				roberta.model.encoder.lm_head.layer_norm.weight
								__lowercase					     =				roberta.model.encoder.lm_head.layer_norm.bias
								__lowercase					     =				roberta.model.encoder.lm_head.weight
								__lowercase					     =				roberta.model.encoder.lm_head.bias

				# Let's check that we get the same results.
				__lowercase					     =				roberta.encode(lowercase				).unsqueeze(0				)  # batch of size 1

				__lowercase					     =				model(lowercase				)[0]
				if classification_head:
								__lowercase					     =				roberta.model.classification_heads['''mnli'''](roberta.extract_features(lowercase				)				)
				else:
								__lowercase					     =				roberta.model(lowercase				)[0]
				print(our_output.shape				,     their_output.shape				)
				__lowercase					     =				torch.max(torch.abs(our_output - their_output				)				).item()
				print(F"max_absolute_diff = {max_absolute_diff}"				)  # ~ 1e-7
				__lowercase					     =				torch.allclose(lowercase				,     lowercase				,     atol=1E-3				)
				print('''Do both models output the same tensors?'''				,     '''🔥''' if success else '''💩'''				)
				if not success:
								raise Exception('''Something went wRoNg'''				)

				pathlib.Path(lowercase				).mkdir(parents=lowercase				,     exist_ok=lowercase				)
				print(F"Saving model to {pytorch_dump_folder_path}"				)
				model.save_pretrained(lowercase				)


if __name__ == "__main__":
		__a    :	Optional[int]         =    argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    """--roberta_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
		)
		parser.add_argument(
		    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
		)
		parser.add_argument(
		    """--classification_head""", action="""store_true""", help="""Whether to convert a final classification head."""
		)
		__a    :	Optional[Any]         =    parser.parse_args()
		convert_xlm_roberta_xl_checkpoint_to_pytorch(
		    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
		)
import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, and builds off the
# `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


__a    :	Union[str, Any]         =    1_6
__a    :	int         =    3_2





def 							UpperCAmelCase		(       lowercase				,     lowercase = 16				):







				"""simple docstring"""



				__lowercase					     =				AutoTokenizer.from_pretrained('''bert-base-cased'''				)
				__lowercase					     =				load_dataset('''glue'''				,     '''mrpc'''				)

				def tokenize_function(lowercase				):
								# max_length=None => use the model max length (it's actually the default)
								__lowercase					     =				tokenizer(examples['''sentence1''']				,     examples['''sentence2''']				,     truncation=lowercase				,     max_length=lowercase				)
								return outputs

				# Apply the method we just defined to all the examples in all the splits of the dataset
				# starting with the main process first:
				with accelerator.main_process_first():
								__lowercase					     =				datasets.map(
								    lowercase				,     batched=lowercase				,     remove_columns=['''idx''', '''sentence1''', '''sentence2''']				,     )

				# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
				# transformers library
				__lowercase					     =				tokenized_datasets.rename_column('''label'''				,     '''labels'''				)

				def collate_fn(lowercase				):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								__lowercase					     =				128 if accelerator.distributed_type == DistributedType.TPU else None
								# When using mixed precision we want round multiples of 8/16
								if accelerator.mixed_precision == "fp8":
												__lowercase					     =				16
								elif accelerator.mixed_precision != "no":
												__lowercase					     =				8
								else:
												__lowercase					     =				None

								return tokenizer.pad(
								    lowercase				,     padding='''longest'''				,     max_length=lowercase				,     pad_to_multiple_of=lowercase				,     return_tensors='''pt'''				,     )

				# Instantiate dataloaders.
				__lowercase					     =				DataLoader(
				    tokenized_datasets['''train''']				,     shuffle=lowercase				,     collate_fn=lowercase				,     batch_size=lowercase				)
				__lowercase					     =				DataLoader(
				    tokenized_datasets['''validation''']				,     shuffle=lowercase				,     collate_fn=lowercase				,     batch_size=lowercase				)

				return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
		from accelerate.test_utils.training import mocked_dataloaders

		__a    :	List[Any]         =    mocked_dataloaders  # noqa: F811





def 							UpperCAmelCase		(       lowercase				,     lowercase				):







				"""simple docstring"""



				if os.environ.get('''TESTING_MOCKED_DATALOADERS'''				,     lowercase				) == "1":
								__lowercase					     =				2
				# Initialize accelerator
				__lowercase					     =				Accelerator(cpu=args.cpu				,     mixed_precision=args.mixed_precision				)
				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				__lowercase					     =				config['''lr''']
				__lowercase					     =				int(config['''num_epochs''']				)
				__lowercase					     =				int(config['''seed''']				)
				__lowercase					     =				int(config['''batch_size''']				)

				__lowercase					     =				evaluate.load('''glue'''				,     '''mrpc'''				)

				# If the batch size is too big we use gradient accumulation
				__lowercase					     =				1
				if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
								__lowercase					     =				batch_size // MAX_GPU_BATCH_SIZE
								__lowercase					     =				MAX_GPU_BATCH_SIZE

				set_seed(lowercase				)
				__lowercase     ,	__lowercase					     =				get_dataloaders(lowercase				,     lowercase				)
				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				__lowercase					     =				AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''				,     return_dict=lowercase				)

				# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
				# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
				# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
				__lowercase					     =				model.to(accelerator.device				)

				# Instantiate optimizer
				__lowercase					     =				AdamW(params=model.parameters()				,     lr=lowercase				)

				# Instantiate scheduler
				__lowercase					     =				get_linear_schedule_with_warmup(
				    optimizer=lowercase				,     num_warmup_steps=100				,     num_training_steps=(len(lowercase				) * num_epochs) // gradient_accumulation_steps				,     )

				# Prepare everything
				# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
				# prepare method.
				__lowercase     ,	__lowercase     ,	__lowercase     ,	__lowercase     ,	__lowercase					     =				accelerator.prepare(
				    lowercase				,     lowercase				,     lowercase				,     lowercase				,     lowercase				)

				# Now we train the model
				for epoch in range(lowercase				):
								model.train()
								for step, batch in enumerate(lowercase				):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device				)
												__lowercase					     =				model(**lowercase				)
												__lowercase					     =				outputs.loss
												__lowercase					     =				loss / gradient_accumulation_steps
												accelerator.backward(lowercase				)
												if step % gradient_accumulation_steps == 0:
																optimizer.step()
																lr_scheduler.step()
																optimizer.zero_grad()

								model.eval()
								__lowercase					     =				0
								for step, batch in enumerate(lowercase				):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device				)
												with torch.no_grad():
																__lowercase					     =				model(**lowercase				)
												__lowercase					     =				outputs.logits.argmax(dim=-1				)
												__lowercase     ,	__lowercase					     =				accelerator.gather((predictions, batch['''labels'''])				)
												# New Code #
												# First we check if it's a distributed system
												if accelerator.use_distributed:
																# Then see if we're on the last batch of our eval dataloader
																if step == len(lowercase				) - 1:
																				# Last batch needs to be truncated on distributed systems as it contains additional samples
																				__lowercase					     =				predictions[: len(eval_dataloader.dataset				) - samples_seen]
																				__lowercase					     =				references[: len(eval_dataloader.dataset				) - samples_seen]
																else:
																				# Otherwise we add the number of samples seen
																				samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
												metric.add_batch(
												    predictions=lowercase				,     references=lowercase				,     )

								__lowercase					     =				metric.compute()
								# Use accelerator.print to print only on the main process.
								accelerator.print(F"epoch {epoch}:"				,     lowercase				)





def 							UpperCAmelCase		(       ):







				"""simple docstring"""



				__lowercase					     =				argparse.ArgumentParser(description='''Simple example of training script.'''				)
				parser.add_argument(
				    '''--mixed_precision'''				,     type=lowercase				,     default=lowercase				,     choices=['''no''', '''fp16''', '''bf16''', '''fp8''']				,     help='''Whether to use mixed precision. Choose'''
				    '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
				    '''and an Nvidia Ampere GPU.'''				,     )
				parser.add_argument('''--cpu'''				,     action='''store_true'''				,     help='''If passed, will train on the CPU.'''				)
				__lowercase					     =				parser.parse_args()
				__lowercase					     =				{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
				training_function(lowercase				,     lowercase				)


if __name__ == "__main__":
		main()